Fiberboard quality classification method based on machine vision

A quality classification and machine vision technology, applied in the direction of instruments, image analysis, computer parts, etc., can solve the problems of threshold segmentation to obtain the fiberboard surface, no defect direction, and morphological feature analysis, so as to improve the timeliness of repair, The effect of reducing labor intensity and accurate test results

Inactive Publication Date: 2022-05-06
泗阳富艺木业股份有限公司
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AI Technical Summary

Problems solved by technology

[0005] The invention provides a fiberboard quality classification method based on machine vision to solve the problem that the direction and morphological features of defects are no

Method used

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  • Fiberboard quality classification method based on machine vision
  • Fiberboard quality classification method based on machine vision
  • Fiberboard quality classification method based on machine vision

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Example Embodiment

[0052] Example 1

[0053]An embodiment of a machine vision-based fiberboard quality classification method of the present invention, such as figure 1 shown, including:

[0054] S101. Obtain a grayscale image of the fiberboard surface

[0055] Use machine vision to collect surface images of fiberboard, perform semantic segmentation on the collected images to remove background interference, and then multiply the semantically segmented images and the collected images for grayscale processing, which is convenient for subsequent operations to analyze the features in the image. extraction and analysis.

[0056] S102. Obtain the sliding window area corresponding to each pixel point

[0057] Taking each pixel in the grayscale image of the fiberboard surface as the center, the sliding window processing is performed to obtain the sliding window area corresponding to each pixel. Sliding window processing is performed on the points to obtain the sliding window area, and the smoothness ...

Example Embodiment

[0072] Example 2

[0073] An embodiment of a machine vision-based fiberboard quality classification method of the present invention, such as figure 2 shown, including:

[0074] S201. Obtain a grayscale image of the fiberboard surface

[0075] Use machine vision to collect surface images of fiberboard, perform semantic segmentation on the collected images to remove background interference, and then multiply the semantically segmented images and the collected images for grayscale processing, which is convenient for subsequent operations to analyze the features in the image. extraction and analysis.

[0076] The camera is arranged, the image of the fiberboard is collected, and the target area in the image is identified and segmented by means of DNN semantic segmentation. The specific process is as follows:

[0077] 1) The data set used is the product image data set collected from the top view, and the styles of fiberboard are various.

[0078] 2) The pixels that need to be s...

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Abstract

The invention relates to the field of artificial intelligence, in particular to a fiberboard quality classification method based on machine vision. The method includes: acquiring a fiberboard surface grayscale image; performing sliding window processing on the grey-scale map on the surface of the fiberboard to obtain a plurality of sliding window areas; calculating the smoothness of a central pixel point of the sliding window; taking the pixel point corresponding to the minimum smoothness as a target pixel point, and obtaining a plurality of target connected domains; obtaining gradient amplitudes of pixel points in each target connected domain to construct a corresponding gradient histogram, calculating the probability that each target connected domain is a defect area, and determining all defect areas; calculating the probability of each gray value as a standard gray value, and determining the standard gray value; and calculating the quality coefficient of the fiberboard according to the gray average value of each defect area, and classifying the quality of the fiberboard. By analyzing the glossiness of the surface image of the fiberboard, the defect that the spatial domain feature is not obvious can be detected, so that the detection result is more accurate, and the product classification accuracy and the repair timeliness are improved; and the production efficiency is effectively improved.

Description

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Claims

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Application Information

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Owner 泗阳富艺木业股份有限公司
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